This report was created the using the behavseqanalyser v0.1.1-alpha, behaviour were grouped using the MITsoft categorisation (date: 2018-02-22).

Ro_testdata, home cage monitoring results

The data is grouped by treatment. Data transformation: data (%age of time spent doing the behavior) transformed using the square root method..

##  first second 
##     10     10

We grouped the variables following the MITsoft argument to get 11 behavior categories. We used the folowing time windows and got 10 x 3 = 30 variables :

  time_reference windowstart windowend windowname
9 lightcondition NIGHT NA nighttime
8 lightcondition DAY NA daytime
7 Bintodark -120 864 full recording

Note that the last window might be truncated if not all dataset is achieving 900 min after light on.

We then run a random forest to get the variables in order of importance to distinguish the groups. We then take the best 20 and run the random forest again (such that the Gini scores obtained will not depend on the initial number of variables). We plot here the table of variables ordered by weight:

Let’s take a teshold of importance (Gini > 0.95) and get all variables satisfying the filter, or at least 8 variables:

Drink1, Drink3, Eat3, Drink2, Hang2, Eat1, Walk2 and Groom1

Plotting

First, lets plot the 2 most discriminative variables following the random forest:

Here, we plot the first two or threecomponents obtained after a ICA performed on the reduced data:

PCA strategy

The PCA strategy shows that the behavior profile of the two groups of animal are not identical.

We performed a PCA on the data and tested whether the groups show a difference in their first component score using a Mann-Whitney or a Kruskal-Wallis rank sum test (if more than 2 groups exists). We plot here the first component in a boxplot:

NB: This strategy is pretty good against type I errors. On the other hand, it may well oversee existing differences.

metadata used for the analysis

project metadata

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Machine learning attempt, SVM

## [1] "No machine learning attempt made."